High throughput screening (HTS) of chemical libraries has become an invaluable tool in the search for drugs [1] and in screening for ancillary activities other than related to a target. Technological advances in synthetic chemistry, robotics, and assay design have greatly increased the efficiency of these screens, leading to a dramatic increase in the number of biologically active small molecule candidates. However, with thousands of potential drug candidates it is becoming increasingly difficult to decide on which candidates to move forward. Current methodologies that analyze a single cell type and single parameter often do not provide sufficient information to make decisions on which compounds are ideally suited to a particular indication. Since the vast majority of resources in preclinical drug discovery are spent on compounds that ultimately fail, it is critical to eliminate as many of these poor leads as early in the drug discovery process as possible. By gathering more compound-specific data earlier, non-specific and toxic lead compounds can be discarded sooner, accelerating drug discovery while minimizing the use of precious resources. Thus the current challenge is generating higher-throughput, more informative, secondary screening assays [2, 3].
Secondary screening assays for cancer or other therapeutics should minimally be able to report on the biological activity, cellular toxicity, membrane permeability, and selectivity of the compound for cancer or other diseased cells relative to normal tissue [4]. The advent of cellular high content screening provides a method of obtaining this information simultaneously [5]. Indicators of cellular toxicity, biological activity and mechanism of action can be examined concurrently in a cellular context providing multiple data points from a single sample. Importantly, high content screening by flow cytometry or microscopy techniques allows these multiple parameters to be measured for each individual cell in the sample [6-12]. Assaying multiple events at the single cell level, particularly with involvement of numerous cells having the same phenotype, produces more robust correlations between signaling events and cellular responses, and enables the researcher to decipher coincident and interrelated effects. These attributes make high content, single-cell assays more than the sum of their parts.
With the ever increasing importance of cancer with an aging population, the development of secondary, high content assays for cancer therapeutics is particularly challenging due to the inherent diversity of this disease. Thousands of different combinations of cellular alterations can lead to oncogenic transformation and disparate cellular phenotypes making it impossible to choose one cell line as a model. As an example, a profile of the 11 breast cancer cell lines derived from patient samples in the MD Anderson Cancer cell line database was analyzed using six parameters from each (Table I). Although similarities exist, this relatively small subset of parameters reveals that no two cell lines are identical. The disparities range from physical attributes such as metastatic potential and invasion, to gene expression and mutation.
Although the validity of using cell lines as model systems is debated, in screening assays they are often a necessity [4] and researchers have found striking similarities between commonly used breast cancer cell lines and fresh tumor explants [13]. Since a representative breast cancer cell line does not exist, candidate compounds are typically tested across panels of cell lines [14]. However no consensus panel is routinely used: the NCI chose nine cell lines to profile against known cytotoxic agents, MD Anderson selected an overlapping yet distinct set (which can be found on the World Wide Web at mdanderson.org), and ‘omics studies by the Ludwig Cancer Institute [15] and the Argonne national laboratories [16] chose still another set of cell lines as representative. Importantly the responses of these cell lines varied up to 100-fold in their sensitivity to specific drugs emphasizing the importance of profiling chemotherapeutic agents across a wide array of sample cell lines [14].
TABLE IinvasionERPAI-Cell Linein vitroexpressionCaspasemetastasis1p53BT-20++n/aneg+n/aBT-474++n/a+−mHs578T+−+++mMCF-7++−+/−+/−wtMDA-MB-231+−+−+mMDA-MB-361−+n/a−−n/aMDA-MB-435+−−++mMDA-MB-468+−n/a−n/amSK-BR-3+/−−n/an/a+mT-47D+/−++n/a−mZR-751+/−+n/an/a−wt
In order to ensure the relevance of secondary screening assays and improve their predictive power, it is necessary to multiplex quantitative, high-content experimental analysis across an array of cell types. Compounds that are generically toxic to non-cancerous cells could be defined by including non-breast cancer cell lines or primary cell samples in the analysis. Drugs highly selective for these other cell lines can be eliminated from the discovery process or assigned to other development programs focused on those particular cellular models. In addition, the profile of responding cell lines is highly informative since many of these cell lines have been genetically and phenotypically characterized [17, 18]. Common features of cell lines that respond or are resistant to treatment with a particular compound can be used to infer mechanism of action of the compound and identify patient populations who may benefit from treatment more than others [19]. Although these types of datasets can be obtained using traditional methods, the amount of test compound, the cost of high content assays, and the manpower necessary to profile the cellular responses of dozens of cell lines against hundreds of samples is prohibitive. Therefore, this type of exhaustive secondary screening is typically only performed on a few lead compounds with large supplies of material available and with a high degree of confidence in its success.
There is, therefore, a crucial need for methods that drastically reduce the cost of screening, permit relatively low amounts of sample candidates to be used, are a rich source of information as to the biological properties of the sample candidate and provide a robust response with a high degree of confidence in the results.